Identify M Subdwarfs from M-type Spectra using XGBoost

Yue, Lili; Yi, Zhenping; Pan, Jingchang; Li, Xintao; Jia, Li

doi:10.1016/j.ijleo.2020.165535

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Xgboost implements a gradient boosting framework based on decision trees, which was proposed by Chen2016 [32]. The library of Xgboost is designed to be highly efficient, flexible and portable [33]. The light gradient boosting machine (LightGBM) algorithm is an efficient distributed gradient boosting tree algorithm [34,35].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Wang

Zhang

et al. 2022

Int J Comput Intell Syst

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Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Wang

Zhang

et al. 2022

Int J Comput Intell Syst

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“…XGBoost can perform three gradient enhancements, e.g., regularization, random, and gradient. Therefore, it can significantly improve the effect of classification or regression problems [71]. The inversion was conducted based on a single sensitive band to explore the potential of spatiotemporal fused images and two inversion models in monitoring SPM.…”

Section: Inversion Model For Spmmentioning

confidence: 99%

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

et al. 2021

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High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ≥ 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management.

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“…XGBoost belongs to the DMLC ("distributed machine learning community"). Its library is designed to be efficient, flexible, and portable [23]. On the other hand, XGBoost also optimizes memory resources and manages missing values during the learning process (sparse aware [24]).…”

Section: Introductionmentioning

confidence: 99%

A robust and consistent stack generalized ensemble-learning framework for image segmentation

et al. 2023

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In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively; our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost); it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms; in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model; they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency.

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Identify M Subdwarfs from M-type Spectra using XGBoost

Cited by 5 publications

References 13 publications

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

A robust and consistent stack generalized ensemble-learning framework for image segmentation

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